Calcification detection using convolutional neural network architectures in Intravascular ultrasound images
Cardiovascular disease is the highest leading to death for Non-Communicable disease. Coronary artery calcification disease is part of cardiovascular disease. The built-in of the plaques and the calcification in the coronary artery inner wall make the blood vessel cross-section area narrow. The stand...
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| Main Authors: | , , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2020-03-01.
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| LEADER | 02905 am a22003253u 4500 | ||
|---|---|---|---|
| 001 | ijeecs20918_13512 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Sofian, Hannah |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Chia Ming, Joel Than |e author |
| 700 | 1 | 0 | |a Muhammad, Suraya |e author |
| 700 | 1 | 0 | |a Mohd Noor, Norliza |e author |
| 245 | 0 | 0 | |a Calcification detection using convolutional neural network architectures in Intravascular ultrasound images |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20918 | ||
| 520 | |a Cardiovascular disease is the highest leading to death for Non-Communicable disease. Coronary artery calcification disease is part of cardiovascular disease. The built-in of the plaques and the calcification in the coronary artery inner wall make the blood vessel cross-section area narrow. The standard practice by the radiologists and medical clinical are by visual inspection to detect the calcification in the intravascular ultrasound image. Deep learning is the current image processing methods that have high potential to detect calcification analysis using convolutional neural network architecture and classifiers. To detect the absence of calcification and presence calcification on the intravascular ultrasound image, using k-fold =10, we compared the three types of convolutional neural network architectures and the seven types of classifiers with the provided ground truth from MICCAI 2011. We used two types of images named as Cartesian Coordinates image and polar reconstructed coordinate image. The classifiers such as Support Vector Machine, Discriminant analysis, Ensembles and Error-Correcting Output Codes obtained the perfect result with value one for Area Under Curve and all the performance measure result, accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Area Under Curve for Naïve Bayes classifier is 0.9967 and for Decision Tree classifier is 0.9994, obtained using the polar reconstructed coordinate image for InceptionresNet-V2 architecture. | ||
| 540 | |a Copyright (c) 2019 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Atherosclerosis, Coronary artery calcification, Deep learning, Convolutional neural network | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 17, No 3: March 2020; 1313-1321 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v17.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20918/13512 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20918/13512 |z Get fulltext |